SPATIAL MODELING OF RAIN-TRIGGERED LANDLSIDES A case study in Southern Leyte Province, Philippines

J. S. M. FOWZE1, D. BUENA2, A. S. DAAG3, M. K.HAZARIKA1 AND

L. SAMARKOON1

1Geoinformatics Center, Asian Institute of Technology, Bangkok, Thailand 2National Mapping and Resource Information Agency, Taguig City, Philippines 3Philippines Institute of Seismology and Volcanology, Quezon City, Philippines

Corresponding author: J.S.Muhammathu.Fowze@ait.ac.th

KEYWORDS: Rain-triggered landslides, SINMAP model, Remote Sensing, GIS, GPS

ABSTRACT: Almost all preparatory and triggering causal factors are well set in place in the case of Philippines to make its sloping terrains vulnerable to landslides; the history of landslide occurrences stands as foolproof evidence. A review of the past events however indicates that rainfall has, inter alia, been offering the triggering effect to cause most of the devastating landslides. A number of such landslides have occurred in the Southern Leyte province of Philippines causing considerable loss of life and damage to properties. Facilitating towards managing the associated landslide risk in Southern Leyte it was aimed at delineating the areas that are prone to rain-triggered landslides. The SINMAP model which combines a mechanistic slope stability model with a steady-state hydrology model was employed in way accomplishing this, much needed, task. Elevation data in the form of a raster GIS layer was the main input for the model. Mapping of past landslide initiation points was also completed with the aid of satellite based remotely sensed data and a GPS survey. The SINMAP model, with a careful attribution of the requisite geotechnical and hydraulic parameters, delineated 47% of the study area which is about 850 km2 to be unstable and quasi-stable under extreme rainfall events recorded in the past. This zonation of landslide hazard was considered to be very satisfactory as 82% of the total recorded landslides were found to fall in the unstable regions. 1. INTRODUTION Although a common geological phenomenon in hilly terrains, landslides attract attention when they become responsible for considerable loss of money and life. In this context, the causes of landslides have long been studied and it is considered that it would be more appropriate to discuss causal factors including both “conditions” and “processes” than “causes” per se alone (Popescu, 1996). Thus ground conditions (e.g. weak materials) are part of the conditions necessary for an unstable slope to develop, to which the environmental criteria of stress, pore water pressure, and temperature must be added. Landslide causal factors are divided according to their effect (preparatory or triggering) and their origin (ground conditions and geomorphological, physical or man-made processes) (WP/WLI, 1994). Unfavourable ground conditions and all processes mentioned above are set in place in the case of the Philippines archipelago to make many parts of it vulnerable to landslides. However, in the search of landslides causes, attention is often focused on those processes which provoke the greatest rate of change and a fast change is often identified as having triggered movement (Popescu, 1996). In line with this, a review of recent catastrophic landslides killing thousands of people and damaging millions worth of properties in Philippines indicates that most of them were rain-

triggered and associated with typhoons or Inter-Tropical Convergence Zones (Orense 2003, Cabria and Catane 2003, Orense 2004, Catane et al 2006). Although disastrous landslide occurrences in Philippines are on the increase, it has been reported that mitigating the risk associated with landslides is not a widespread practice in Philippines (Zarco et al 2007). Irrespective of the type of danger, analysis of previous events and prediction of future occurrences is the key to any activity pertaining to mitigation and management of the associated risk. The undertaking of this task has very well been supported by the ever developing remote sensing and GIS technologies and this paper presents the utilization of model, based on remote sensing and GIS for rain-triggered landslide hazard zonation of a selected area in Philippines. 2. STUDY AREA

Figure1: Study Area

The study area which covers the Sogod, Silago, Hinuangan, Libagon, St. Bernard, and St. Juan townships and Panoan Island has an area of approximately 850 km2. It is located within eastern longitudes 125º00’ to 128º18’ and northern latitudes 9º55’ to 10º30’. The climate of the region is characterized by pronounced rainfall from November to January with a historic average monthly rainfall of 178 mm. The rainy period is extended by La Nina phenomeneon. The area is underlain by volcanic rocks. Conversion of land cover from primary forest to coconut plantation is a common feature identified in this area. Two of the recent and disastrous landslides had occurred in this area; namely, the Panoan island landslide of 19th December 2003 and the Guinsaugon landslide of 17th February 2006. 3. METHODOLOGY There are many approaches to assess the stability of slopes and landslide hazards. However, a promising approach to model the rain-triggered landslides at a regional scale combines a mechanistic infinite slope stability model with hydrologic models. The Stability INdex MAPping

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LANDSAT TM IMAGE OF SOUTHERN LEYTE

DATE OF ACQUISITION: June 29, 1992

STUDY AREA

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(SINMAP) model based on the above approach was, therefore, utilized in this study. It is a raster based slope stability predictive tool. A detailed discussion of the model is given in Pack et al. (1998). 3.1. Model Variables and Model Parameters The equation for factor of safety (FS) based on which SINMAP calculates the stability indices takes the following form after mathematical manipulations. 'θ' is the angle of slope (See Fig. 1) and ‘φ' is the effective angle of internal friction. ‘C’, ‘w’, and ‘r’ denote the dimensionless cohesion, wetness index, and density ratio respectively and are defined in the context of SINMAP as shown below. ‘R’ and ‘T’ which appear in the wetness index equation, eq. (3), are the effective recharge and material transmissivity. ‘a’ which also appears in the same equation stands for the specific catchment area defined as the upslope area per unit contour length with units m2/m. As could be seen from Fig 1, ‘h’ is the depth to the plane of failure. Definition of SINMAP model for a particular region of interest, therefore, requires 2 variables and 3 parameters; Variables: ‘θ’ and ‘a’ Parameters: C, φ, and the ratio R/T The variables are implicitly input in the form of a DEM for the model to calculate them from in-built modules for each and every pixel. At this point, it needs to be emphasized that it would not simply be possible to model a wide range of material and climatic conditions by specifying one single value for the parameters. SINMAP model accounts for this uncertainty as another attracting feature, and is so developed to input these parameters by specifying lower bound and upper bound values assuming a uniform probability distribution. Further, the temporal and spatial variability is accounted for by the appropriate choice of parameters and variables. The water to soil density ratio ‘r’ defined by equation (3) as the water to soil density ratio is normally assumed to be 0.5 An inventory of landslide initiation points is also needed as crucial information for validating the model output. 3.2 Model Inputs With reference to Section 3.1 above, the SINMAP model inputs could be summarized as follows;

(i) DEM grid theme of the study area,

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(ii) geotechnical data: range of C values and range of φ values (iii) hydrological data: range of T/R as required by the software (iv) Inventory of past landslides points theme

3.2.1 DEM Grid Theme The main input to the model, DEM grid theme, created from a digitized topographic map of scale 1:50,000 collected from the National Mapping and Resource Information Agency of Philippines. The contours were of 20 m interval. 3.2.2 Geotechnical Data During the study, it was realized that despite many landslide occurrences, attempts to gather and document especially geotechnical data which are required for landslide hazard analyses were at a minimal level. A field visit was, therefore, made with a limited scope. Landslide locations in accessible areas were inspected and soil samples were collected from 21 landslide locations to give a cross section of geotechnical data pertaining to the study area. Laboratory index tests were performed on the collected samples at the Soil Laboratory of the University of Philippines and the results along with observations on ‘h’ were used to infer realistic ranges of C and φ values. As the observations on most of failed slopes indicated that the surface of rupture is below the root zone, the contribution of root cohesion, Cr, was assumed to be zero. 3.2.3 Hydrological data The denomenator T of the hydrologic data input is defind as the vertical integral of the hydraulic conductivity of soil and is determined by

where ks is the hydraulic conductivity or the permeability of the material of interest. The results of the laboratory index tests were also made use of for inferring realistic values of permeability and then to calculate T. The numerator R, in the context of SINMAP is defined as the effective recharge for a critical period of wet weather likely to trigger landslides. Although, the effective recharge is given by

Rainfall – Evapotranspiration – Deep Percolation

the evapo-transpiration and deep percolation were assumed negligible in the analysis. The threshold value of rainfall, 100 mm in 3 days, established by the traffic advisory in Philippines as that triggers landslides was adopted as the lower bound rainfall value while the extreme event recorded in the history of the study area i.e. about 700 mm in 3 days, was adopted as the upper bound rainfall value. 3.2.4 Inventory of Past Landslides Points Theme. Details of some past landslides were available with NAMRIA. In addition, some other recent landslide points were identified through visually interpreting a SPOT-5 imagery of June 2006. Landslide initiation points as required for the input was carefully extracted by overlaying the contour data on the satellite imagery. A GPS survey carried out during the field visit complemented

hkT s ×= )5(−−−−−−

)6(−−−−−−

other landslide initiation points to make a total of them amount to 61. 3.3 EXECUTION OF SINMAP Five main regions were identified during the field visit based upon the landcover type as could be attributed with different ranges of parameters. A multi region theme was accordingly prepared using the supervised classification technique with the Maximum Likelihood Classifier from the Landsat image of 2002. The model was then executed in the ArcView environment with parameters that best fit the field conditions. 5. RESULTS AND DISCUSSION Figure 2 shows the Stability Index (SI) map derived from the SINMAP model. Stability index values, based on the values of factor of safety, are 0.0 or greater, with 1.0 indicating some level of stability. Six broad classes have been defined as shown in Table 1 which could be visualized as different hazard zones.

Table 1: Stability States The statistical summary corresponding to the SI map is shown in Table 02. The hazard analysis delineated 75.8 km2 (9% of the total study area) as quasi-stable, 227.5 km2 or (27%) as lower-threshold, and 92.7 km2 or (11%) as upper-threshold zones. These low stability, low instability, and medium instability zones make a total of 47% of the total study area. Results of the overlay of past landslide points on the SI map for the purpose of validating the model output is also included in the same table. It could be seen that the largest number of landslides (a total of 39 landslides) are found in the “lower threshold” zone. The “upper threshold” zone includes 11 landslides while 3 landslides are found to fall in the ‘quasi-stable’ zone. Collectively, 82% of the inventoried landslides (61) fall in the quasi or low stability and unstable regions. This indicates that the efforts for the spatial modeling of landslides in the selected study area and thereby to predict the future events using the SINMAP model have been successful.

SI Class Predicted State SI>1.5 1 Stable 1.5>SI>1.25 2 Moderately Stable 1.25>S>1.0 3 Quasi-Stable 1.0>SI>0.5 4 Lower-Threshold 0.5>SI>0.0 5 Upper-Threshold 0.0>SI 6 Defended

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Stability IndexStableModerately StableQuasi-stableLower ThresholdUpper ThresholdDefended

LEGEND:%U Landslide points

Figure 2: Stability Index Map

Moderately Quasi- Lower- Upper-

Stable Stable Stable Threshold Threshold

Defended Total

Region 1 0 0 0 1 0 0 1 Region 2 0 0 0 3 0 0 3 Region 3 0 0 0 0 0 0 0 Region 4 4 1 3 27 8 0 43 Region 5 3 0 0 8 3 0 14

# Landslides 7 1 3 39 11 0 61 % of Slides 11 2 5 64 18 0 100

Area(km2) 379.2 67.4 75.8 227.5 92.7 0.0 842.7 % Area 45 8 9 27 11 0 100

Table 2: Statistical Summary for the Analyzed Area 6.0 CONCLUSION A remote sensing and GIS based deterministic slope stability model, SINMAP, was successfully utilized to predict the landslide occurrences in the Southern Leyte island of Philippines, which are mostly rain-triggered. Modeling was carried out within a range of rainfall between and including established threshold value of rainfall that triggers landslides in the study area, and the extreme event rainfall recorded in the history. Analytical results indicate that 53% of the study area is stable state whilst the remaining 47% is in quasi stable and lower and upper threshold states. This zonation is considered to be very satisfactory as 82% of the inventoried landslides fall in the above zones. In the light of this results, it is suggested that in the zones identified as quasi-stable and threshold, consequence analyses be carried out to analyze and assess the associated risk. Having identified the risk, the same could be minimized by adopting either one or a combination of the following; modification of slope geometry, drainage, retaining structures, internal slope reinforcement. ACKNOWLEDGEMENT The authors wish to thank the Japan Aerospace Exploration Agency (JAXA) for providing financial support and the Philippines Institute of Volcanology and Seismology and the National Mapping and Resource Information Agency for providing with the data used in this study. REFERENCES Popescu, M.E. (1996). “From Landslide Causes to Landslide Remediation, Special Lecture.” Proc. 7th Int. Symp. on Landslides, Trondheim, 1:75-96 WP/WLI: International Geotechnical Societies’ UNESCO Working Party on World Landslide Inventory. Working Group on Landslide Causes – Popescu, M.E. Chairman. (1994). A suggested Method for landslide reporting landslide causes, Bulletinn IAGE, 50:71-74 Cabria, H.B., and Catane, S.G., 2003. The 19 December Landslides in Panoan Island, Southern Leyte, Philippines. QRT Report, National Institute of Geological Sciences, University of the Philippines, Dilman, Quezon City. Zarco, M.A.H., Catane, S.G., and Gonzalez, .M.2007.State of the practice in slope mitigation measures: Focus on the Philippines, Proc. 2nd Regional Training Course, RECLAIM Phase II, Phuket Thailand. Pack, R T, D G Tarboton, C N Goodwin (1998) Terrain Stability Mapping with SINMAP, technical description and users guide for version 1.00," Report Number 4114-0, Terratech Consulting Ltd., Salmon Arm, B.C., Canada (www.tclbc.com)